Method and apparatus for detecting a desired signal in the presence of an interfering signal

ABSTRACT

In accordance with an embodiment, there is provided a method comprising receiving, at a receiver, a desired signal and an interfering signal, wherein the interfering signal was transmitted with a modulation unknown to the receiver; identifying a likely modulation corresponding to the modulation with which the interfering signal was transmitted; and decoding the desired signal using a modulation dependent multiple-input multiple output (MIMO) detection, wherein the modulation dependent MIMO detection is based at least in part on the identified likely modulation corresponding to the modulation with which the interfering signal was transmitted, wherein the modulation dependent MIMO detection includes maximum likelihood (ML) detection.

CROSS-REFERENCE TO RELATED APPLICATION

The present disclosure claims priority to U.S. Provisional PatentApplication No. 61/551,339, filed on Oct. 25, 2011, which isincorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to multiple-input multiple-output (MIMO)systems, and more specifically, to receivers implemented in MIMOsystems.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

In wireless communication systems, multiple-input and multiple-output(MIMO) refers to an use of multiple antennas at both a transmitter and areceiver to improve communication performance. MIMO technology hasattracted attention in wireless communications, because MIMO offerssignificant increase in data throughput and link range withoutadditional bandwidth or increased transmit power. MIMO achieves this by,for example, spreading the same total transmit power over the antennasto achieve an array gain that improves the spectral efficiency (e.g.,more bits per second per hertz of bandwidth) or to achieve a diversitygain that improves a link reliability (e.g., reduced fading). Because ofthese properties, MIMO is an important part of various modern wirelesscommunication systems.

SUMMARY

In accordance with an embodiment, there is provided a method comprisingreceiving, at a receiver, a desired signal and an interfering signal,wherein the interfering signal was transmitted with a modulation unknownto the receiver; identifying a likely modulation corresponding to themodulation with which the interfering signal was transmitted; anddecoding the desired signal using a modulation dependent multiple-inputmultiple output (MIMO) detection, wherein the modulation dependent MIMOdetection is based at least in part on the identified likely modulationcorresponding to the modulation with which the interfering signal wastransmitted, wherein the modulation dependent MIMO detection includesmaximum likelihood (ML) detection.

There is also provided, in accordance with an embodiment, a systemcomprising one or more antennas configured to receive a desired signaland an interfering signal, wherein the interfering signal wastransmitted with a modulation unknown to the system; and a dataprocessing unit coupled to the one or more antennas, wherein the dataprocessing unit is configured to (i) identify a likely modulation or thelikelihood of a modulation corresponding to the modulation with whichthe interfering signal was transmitted, and (ii) decode the desiredsignal using a modulation dependent detection, wherein the modulationdependent detection is based at least in part on the identified likelymodulation or the identified likelihood of the modulation correspondingto the modulation with which the interfering signal was transmitted,wherein the modulation dependent detection includes maximum likelihood(ML) detection.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure will be readily understood by thefollowing detailed description in conjunction with the accompanyingdrawings. To facilitate this description, like reference numeralsdesignate like elements.

FIG. 1 is a block diagram illustrating a communications system.

FIG. 2 is a flowchart illustrating a method of decoding a desired signalusing maximum likelihood detection.

FIG. 3 is a block diagram illustrating a method of decoding a desiredsignal using interference cancellation.

DETAILED DESCRIPTION

Described herein are example systems, components, and methods that maybe used in conjunction with MIMO (multiple-input/multiple-output) and/orMU-MIMO (multi-user MIMO) techniques. The following description merelyprovides examples and is in no way intended to limit the disclosure, itsapplication, or uses.

A MIMO receiver (also referred to herein more generally as a “receiver”)typically receives a signal that carries a plurality of effective datasignals, only some of which is intended for the receiver. The effectivedata signal intended for the receiver, referred to herein as the desiredsignal, may be decoded using a corresponding channel transfer matrix,also referred to as a channel matrix. Other effective data signals,referred to as interfering signals, are associated with differentchannel matrices. In some cases, the channel matrices of interferingsignals may be known or may be estimated.

The effective signals described above may be transmitted using differentmodulations. Available modulations may include, for example, variousimplementations of quadrature amplitude modulation (QAM), quadraturephase shift keying (QPSK), and others. In some situations, themodulations used by interfering signals may not be known to a receiver.This may be the case, for example, when interfering signals are theresult of multi-user MIMO or co-channel interference.

This disclosure is directed to mitigating interference in an MIMOenvironment in which a receiver is able to identify presence ofinterfering signals and their corresponding channel matrices, but inwhich the modulations of the interfering signals are not known to thereceiver.

FIG. 1 illustrates an example multiple-usermultiple-input/multiple-output (MU-MIMO) communication system 100. Threemobile devices (e.g., mobile devices that communicate data by way ofradio transmissions) U₁, U₂, and U₃ are interfacing with the system 100.The system 100 includes two base stations (e.g., cellular telephonetransmitter/receivers towers) BS₁ and BS₂. An approximate boundary foreach base station's range, referred to as the base station's “cell”, isshown as a circle surrounding the base station. While the systems andmethods herein will be described in the context of a multiuser MIMOenvironment, the systems and method may be used in any communicationsystem in which a signal detection technique accounts for the potentialexistence of a number of interfering communication channels.

Base station BS₁ communicates with the mobile device U₁ by way of aneffective signal, which is defined or characterized by a channel matrixh_(1,1). BS₁ also communicates with the mobile device U₂ by way ofeffective signal defined or characterized by a channel matrix h_(2,2).The signal being transmitted to U₂ may interfere with the signal beingtransmitted to U₁. This intra-cell interfering signal travels along aneffective channel characterized by a channel matrix h_(1,2) to U₁.

Because many modern wireless communication systems have adjacent cellstransmitting in the same frequency bands, mobile devices are alsosubject to inter-cell interference. The base station BS₂ exchangessignals with the mobile device U₃ by way of an effective channelcharacterized by the channel matrix h_(3,3). This signal also interfereswith the signal being transmitted by BS₁ to U₁.

The following discussion focuses on operations performed at a receiverthat receives multiple signals, each of which is characterized by adifferent channel matrix h_(i), where the suffix is an indexcorresponding to an individual signal and channel matrix. Thus, if areceiver receives two effective signals x₁ and x₂, those effectivesignals may be characterized by channel matrices h₁ and h₂,respectively. While the following discussion focuses on the two datastreams, the teachings of this disclosure applies to systems that canhandle a larger number of data streams.

One embodiment of a wireless signal processing unit 102 is shown inFIG. 1. The wireless signal processing unit 102 is part of the mobiledevice U₁. The wireless signal processing unit 102 includes a MIMOtransmitter/receiver radio 104. The radio 104 includes four antennas 106that are configured to receive a signal r containing multiple effectivesignals x. The radio 104 provides the signal r to a baseband processor108. The baseband processor 108 combines performing signal detectionand/or filtering based on information about the channels of the receivedsignal to estimate a desired signal meant for the particular mobiledevice U₁.

The signal processing unit 102 from FIG. 1 may be implemented on a chipincluding one or more integrated circuits configured to perform one ormore of the functions described herein. The signal processing unit 102may be implemented in a computing device, for example, a computer, alaptop, a server, a cell phone, a hand held computing device, or othertype of device that uses memory. The baseband processor 108 may be partof the same device as the signal processing unit 102 or may be externalto the apparatus. The signal processing unit 102 may be configured byway of instructions stored in associated computer-readable media, whichare executable by the processing unit 102 to perform the computations,calculations, and/or operations described below.

The general characteristics of MIMO transmission, assuming a number ofusers K, may be characterized as follows:

${r(i)} = {{{{H(i)}{x(i)}} + {n(i)}} = {{{{H_{1}(i)}{x_{1}(i)}} + {\sum\limits_{k = 2}^{K}{{H_{k}(i)}{x_{k}(i)}}} + {{n(i)}\mspace{14mu} 1}} \leq i \leq L}}$

r: N_(R)×1 received vector

$H\text{:}\mspace{14mu} N_{R} \times {\sum\limits_{k = 1}^{K}N_{T,k}}$effective MIMO channel, where H=[H₁ . . . H_(K)]

$x\text{:}\mspace{14mu}{\sum\limits_{k = 1}^{K}{N_{T,k} \times 1}}$transmitted vector

x₁: transmitted signal for the desired user of modulation M⁽¹⁾

x_(k): transmitted signal for the interferer of modulation M^((k)), k=2. . . K

n: white noise at receiver, such that E(nn^(H))=σ²I

N_(R): number of receiver antennas

N_(T,k): number of transmit antennas (number of spatial streams) of eachuser

i: a transmission instance out of total L transmissions, such as anindex of subcarriers.

Equation 1

For ease of explanation, the following discussion will assume two rank-1users, resulting in the following simplification of Equation 1:r(i)=H(i)x(i)+n(i)=h ₁(i)x ₁(i)+h ₂(i)x ₂(i)+n(i)1≦i≦L

-   -   H: N_(R)×2 effective MIMO channel, where H=[h₁ h₂]    -   x: 2×1 transmitted vector    -   x₁: transmitted signal for the desired user of modulation M⁽¹⁾

x₂: transmitted signal for the interferer of modulation M⁽²⁾, M⁽²⁾εΨ

Equation 2

The channel or channel matrix associated with the desired signal x₁ isrepresented as h₁. The channel or channel matrix associated with theinterfering signal x₂ is represented as h₂.

It is assumed in the following discussion that the existence of theinterfering signal is known, and that the channel matrix h₂ of theinterfering channel has been obtained from advanced channel estimationtechniques. For example, the channel matrix h₂ may be calculated as theorthogonal RS sequence of transmission mode 8 of the “Long TermEvolution” (LTE) Rel. 9 specification.

It is further assumed that the modulation M⁽²⁾ of the interfering signalx₂ is chosen from a finite set ψ of possible modulations, and that thecomposition of this set is known by the receiver. For example, if thereceiver knows the wireless system from which the interference comesfrom, the receiver may also know a set of all possible modulations forthe interference. However, the particular modulation M⁽²⁾ being used bythe interfering signal x₂ is not known at the receiver.

The interfering effective signal may be associated with a co-scheduledMU-MIMO channel, or may be associated with a co-channel in a neighboringsystem (e.g., a neighboring cell for a cellular system, or a nearbyaccess point for a WiFi system).

Generally, an MMSE receiver can be applied to detect a received signalx₁, without knowledge of the modulation of an interfering signal x₂, asfollows:{tilde over (x)} ₁ =[H ^(H)(HH ^(H)+σ² I)⁻¹]_((1,:)) r=h ₁ ^(H)(HH^(H)+σ² I)⁻¹ r

{circumflex over (x)} ₁ =Q({tilde over (x)} ₁ ;M ⁽¹⁾)

Equation 3

The MMSE receiver of Equation 3 works regardless of the modulation usedin the interfering signal, and is a conventional solution forinterference mitigation. In Equation 3, Q(.;M) is a slicer based on themodulation M, as an example for decision. In some embodiments, a hardslicer is replaced by a soft decision (e.g., a bit-wiselog-likelihood-ratio, or LLR) calculator taking the MMSE receiver output{tilde over (x)}₁ as input.

In cases where the modulation M⁽²⁾ of an interfering signal is known, amodulation-dependent receiver may outperform a modulation independentreceiver such as the MMSE receiver, in which the interference is treatedas Gaussian noise. For example, ML detection as an example ofmodulation-dependent receiver can be applied as follows:

$\begin{matrix}{{\hat{x}}_{1} = {\underset{{x_{1} \in \Omega^{(M^{(1)})}},{x_{2} \in \Omega^{(M^{(2)})}}}{\arg\;\min}{{r - {h_{1}x_{1}} - {h_{2}x_{2}}}}^{2}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$Equation 4 shows an example of hard decision using ML criterion. A softdecision (such as bit-wise LLR) can also be used in some embodimentsfollowing ML criterion. Other examples of modulation dependent receiverinclude decision-feedback equalizer, interference cancellation, and/orthe like. In some of the embodiments discussed herein later, MLdetection is used as an example.

However, the modulation M⁽²⁾ of the interfering effective signal x₂ isoften unknown. Described below are various methods, techniques, andenhancements for performing ML detection even in situations where themodulation M⁽²⁾ of the interfering signal is unknown.

FIG. 2 illustrates an example of decoding a desired signal x₁ associatedwith a receiver channel h₁, in the presence an interfering signal x₂associated with an interfering channel h₂. Although the example shows asingle interfering signal, the described techniques may be used in asimilar fashion to decode the desired signal x₁ in the presence ofmultiple interfering channels.

An action 202 comprises receiving the desired signal x₁ and theinterfering signal x₂. The signals may be generated by differenttransmitters or base stations, and/or from different antennas of asingle transmitter or base station. The modulation of the interferingsignal x₂ is unknown to the receiver.

An action 204 comprises identifying a likely modulation of theinterfering signal x₂. This may be performed in a number of differentways, as will be described below. In certain embodiments, the action 204may comprise estimating, calculating, or otherwise determining amodulation {circumflex over (M)}⁽²⁾ that is assumed to be used by theinterfering channel h₂.

The action 204 may be generally characterized by the following equation:{circumflex over (M)} ⁽²⁾ =f(h ₁ ,h ₂ ,r,M ⁽¹⁾,σ²)

Equation 5

An action 206 comprises applying maximum likelihood (ML) detection,based at least in part on the assumed modulation {circumflex over(M)}⁽²⁾, in order to obtain the desired signal x₁. An example of MLdetection to determine a hard estimate of x₁ is generally characterizedby the following equation:

$\begin{matrix}{{\hat{x}}_{1} = {\underset{{x_{1} \in \Omega^{(M^{(1)})}},{x_{2} \in \Omega^{(M^{(2)})}}}{\arg\;\min}{{r - {h_{1}x_{1}} - {h_{2}x_{2}}}}^{2}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$where Ω is the set of possible or available modulations that may havebeen used by the desired signal and the interfering signal. A bit-wisesoft decision can also be determined in some embodiments in a similarfashion with the assumed modulation {circumflex over (M)}⁽²⁾. In thefollowing discussion and as an example, hard decision is used withoutexcluding the application of the techniques to soft decision.

The actions 204 and 206 may in some cases be combined, usingcalculations that jointly determine both the desired signal x₁ and theassumed modulation {circumflex over (M)}⁽²⁾ as follows:{{circumflex over (x)} ₁ ,{circumflex over (M)} ⁽²⁾ }=G(h ₁ ,h ₂ ,r,M⁽¹⁾,σ²)

Equation 7

The calculations of Equations 5, 6, and 7 are implemented to minimize acost function over all possible modulations:

$\begin{matrix}{{\hat{M}}^{(2)} = {\min\limits_{M^{(2)} \in \Psi}{f_{M^{(2)}}( {h_{1},h_{2},r,M^{(1)},\sigma^{2}} )}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$Joint ML Sequence and Modulation Detection

An example of applying Equation 7 is that the actions 204 and 206 may beperformed jointly, by evaluating the a-posteriori probabilities of thedifferent available modulations of the set ψ in combination with thedecoded desired signal x₁. Evaluation of a-posteriori probabilities maybe performed in several different ways, as indicated along the rightside of FIG. 2, under the heading “Joint”.

Log-MAP

A method referred to herein as “Log-MAP” may be used to maximize thea-posteriori probability of the desired signal x₁ and the assumedmodulation {circumflex over (M)}⁽²⁾ of the interfering signal x₂, giventhe signal r, as follows:

$\begin{matrix}{\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} = {\underset{M^{(2)} \in \Psi}{\arg\;\max}\;{P( {{{x_{1}(1)}\mspace{20mu}\ldots\mspace{14mu}{x_{1}(L)}}, M^{(2)} \middle| r } )}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$Where L is the number of signals in the sequence.

Eq. 9 may be implemented, in an embodiment, as follows:

                                      Equation  10$\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} = {{\underset{M^{(2)},x_{1},x_{2}}{argmax}( \frac{1}{M^{(2)}} )}^{L}{\prod\limits_{i = 1}^{L}( {\sum\limits_{m = 1}^{M^{(1)}}{\sum\limits_{n = 1}^{M^{(2)}}{\exp( {- \frac{{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}} )}}} )}}$

Equation 10 assumes that any residual noise is Gaussian, and that eachof the available modulations has an equal a-priori probability of beingused by the interfering channel. Equation 10 finds the a-posterioriprobabilities of all possible modulations, and then selects themodulation having the highest a-posteriori probability.

In some situations, certain modulations are more likely to be used thanothers. In these situations, Eq. 9 may be implemented as follows, toaccount for the differing a-priori probabilities of each modulation:

                                      Equation  11$\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} = {\underset{M^{(2)},x_{1},x_{2}}{argmax}{P( M^{(2)} )}( \frac{1}{M^{(2)}} )^{L}{\prod\limits_{i = 1}^{L}( {\sum\limits_{m = 1}^{M^{(1)}}{\sum\limits_{n = 1}^{M^{(2)}}{\exp( {- \frac{\begin{matrix}{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} -}} \\{{{h_{2}(i)}x_{2}^{(n)}}}^{2}\end{matrix}}{\sigma^{2}}} )}}} )}}$In the following discussion and as an example, the equal a-prioriprobabilities of each modulation is used without excluding the scenariosof unequal a-priori probabilities of each modulation.Sum-Max-Log-MAP

The Log-MAP method of maximizing the a-posteriori probability of thedesired signal x₁ and the modulation {circumflex over (M)}⁽²⁾ of theinterfering channel may be refined using a method referred to herein as“Sum-Max-Log-MAP”. Specifically, equations 10 and 11 can be implementedin a less complex fashion by approximating the sum of the a-posterioriprobabilities, where a core part of Equations 10 and 11 is replaced by asimplification. In particular, the term

$\frac{1}{M^{(2)}}{\sum\limits_{m = 1}^{M^{(1)}}{\sum\limits_{n = 1}^{M^{(2)}}{\exp( {- \frac{{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}} )}}}$of Equations 10 and 11 may be replaced by the following expression:

$\begin{matrix}{\frac{1}{M^{(2)}}{\exp( {- \frac{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}} )}} & {{Equation}\mspace{14mu} 12}\end{matrix}$The Approximation Leads to Little Performance Loss, for Example, whenSignal to Noise Ratio (SNR) is Relatively High.

For example, when the probability of each M2 is equal, the desiredsignal x₁ and the modulation {circumflex over (M)}⁽²⁾ of the interferingsignal x₂ are then obtained by comparing the average minimum distanceper modulation with an offset:

                                      Equation  13 $\begin{matrix}{\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} = {{\underset{M^{(2)},{x_{1}x_{2}}}{argmax}( \frac{1}{M^{(2)}} )}^{L}{\exp( {- {\sum\limits_{l = 1}^{L}\frac{\begin{matrix}{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} -}}} \\{{{h_{2}(i)}x_{2}^{(n)}}}^{2}\end{matrix}}{\sigma^{2}}}} )}}} \\{= {{argmin}( {{\frac{1}{L}{\sum\limits_{i = 1}^{L}\frac{\begin{matrix}{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} -}}} \\{{{h_{2}(i)}x_{2}^{(n)}}}^{2}\end{matrix}}{\sigma^{2}}}} + {\log( M^{(2)} )}} )}}\end{matrix}$Mean-Max-Log-MAP

Alternatively, for example, when the SNR is relatively low, Equations 10and 11 can be implemented using a method referred to herein as“Mean-Max-Log-MAP”, which comprises approximating the mean probabilityby the maximum probability. Specifically, the term

$\frac{1}{M^{(2)}}{\sum\limits_{m = 1}^{M^{(1)}}{\sum\limits_{n = 1}^{M^{(2)}}{\exp( {- \frac{{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}} )}}}$of Equations 10 and 11 is replaced by the following expression:

$\begin{matrix}{\exp( {- \frac{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}} )} & {{Equation}\mspace{14mu} 14}\end{matrix}$

For example, when the probability of each M2 is equal, the desiredsignal x₁ and modulation {circumflex over (M)}⁽²⁾ of the interferingsignal are then obtained as follows:

$\begin{matrix}{\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} = {\underset{M^{(2)},x_{1},x_{2}}{\arg\;\min}( {\frac{1}{L}{\sum\limits_{i = 1}^{L}\frac{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}}} )}} & {{Equation}\mspace{14mu} 15}\end{matrix}$Max-Log-MAP-Scale

In cases where it is difficult or impractical to make a decisionregarding SNR, the Equations 10 and/or 11 may be implemented as follows,independently of SNR values:

$\begin{matrix}\begin{matrix}{\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} = {\underset{M^{(2)},x_{1},x_{2}}{\arg\;\max}( \frac{1}{M^{(2)}} )^{L}{\prod\limits_{i = 1}^{L}\;( {\frac{1 + {\alpha( {H,M^{(1)},M^{(2)},\sigma^{2}} )}}{M^{(2)}} \cdot} }}} \\ {\exp( {- \frac{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}} )} ) \\{= {\underset{M^{(2)},x_{1},x_{2}}{\arg\;\max}\frac{1}{L}{\sum\limits_{i = 1}^{L}( {\frac{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}} -} }}} \\{ {\log( {1 + {\alpha( {H,M^{(1)},M^{(2)},\sigma^{2}} )}} )} ) + {\log( M^{(2)} )}}\end{matrix} & {{Equation}\mspace{14mu} 16}\end{matrix}$

In Equation 16, the term

$\frac{1 + {\alpha( {H,M^{(1)},M^{(2)},\sigma^{2}} )}}{M^{(2)}}$is a scaling factor that is used to scale the subsequent exponentialterm of Equation 16. The scaling factor can be approximated and storedas a lookup table to lessen the complexity of calculating Equation 16.The desired signal x₁ and the modulation {circumflex over (M)}⁽²⁾ of theinterfering channel x₂ may be calculated in this scenario as follows:

$\begin{matrix}{\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} = {{\underset{M^{(2)},x_{1},x_{2}}{\arg\;\min}\frac{1}{L}{\sum\limits_{i = 1}^{L}( \frac{\min{{{r(i)} - {{h_{1}(i)}x_{1}^{(m)}} - {{h_{2}(i)}x_{2}^{(n)}}}}^{2}}{\sigma^{2}} )}} + {I( {{r;x_{1}}, x_{2} \middle| M^{(1)} ,M^{(2)},H} )}}} & {{Equation}\mspace{14mu} 17}\end{matrix}$Log-MAP-Best-K

As yet another example, a balance between accuracy and complexity may beobtained when calculating Equations 10 and 11 using a method referred toherein as “log-MAP-Best-K”, which comprises computing only the largest Kterms in the sum of conditional probabilities, as follows:

$\begin{matrix}{\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{\hat{M}}^{(2)}} \} \approx {\quad{{\underset{M^{(2)},x_{1},x_{2}}{\arg\;\max}\frac{1}{M^{(2)}}{\sum\limits_{k = 1}^{K{(M^{(2)})}}{{\exp( {- \frac{{{{r(i)} - {{h_{1}(i)}x_{1}^{({k,m})}} - {{h_{2}(i)}x_{2}^{({k,n})}}}}^{2}}{\sigma^{2}}} )}{s.t.\mspace{14mu}{{{r(i)} - {{h_{1}(i)}x_{1}^{({1,m})}} - {{h_{2}(i)}x_{2}^{({1,n})}}}}^{2}}}}} \leq {{{r(i)} - {{h_{1}(i)}x_{1}^{({2,m})}} - {{h_{2}(i)}x_{2}^{({2,n})}}}}^{2} \leq {\ldots\mspace{14mu}{{{r(i)} - {{h_{1}(i)}x_{1}^{({M^{(1)},M^{(2)},m})}} - {{h_{2}(i)}x_{2}^{({M^{(1)},M^{(2)},n})}}}}^{2}}}}} & {{Equation}\mspace{14mu} 18}\end{matrix}$The depth of this calculation may be dependent on one or more factorsincluding modulation, SNR, and/or the like, and may be adjusted in thereal-time.Sequential Modulation and Sequence Detection

In certain embodiments, the assumed modulation M⁽²⁾ may be initiallydetermined, and may then be used as the basis for decoding the desiredsignal x₁. This is referred to herein as sequential modulation andsequence detection. The assumed modulation M⁽²⁾ may be calculated orestimated in various ways, as indicated on the left side of FIG. 2 underthe heading “Sequential”.

The techniques for joint ML sequence and modulation detection mentionedabove can also be implemented in the fashion of sequential modulationand sequence detection discussed hereafter. For example, the modulationof M2 can be detected by maximizing the a-posteriori probability of thedesired signal x₁ and the assumed modulation {circumflex over (M)}⁽²⁾ ofthe interfering signal x₂, given the signal r, as follows:

$\begin{matrix}{{\hat{M}}_{2} = {\underset{M_{2} \in \Psi}{\arg\;\max}\;{P( {{{x_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{x_{1}(L)}}, M_{2} \middle| r } )}}} & {{Equation}\mspace{14mu} 19}\end{matrix}$And then the determined M2 is used for a ML detection of the desiredsequence. That is,

${\hat{x}}_{1} = {\underset{{x_{1} \in \Omega^{(M^{(1)})}},{x_{2} \in \Omega^{(K)}}}{\arg\;\min}{{r - {h_{1}x_{1}} - {h_{2}x_{2}}}}^{2}}$The equations [9] to [18] can be similarly implemented in the sequentialfashion. Additionally, some specific examples for sequential modulationand sequence detection are discussed as follows.Fixed Modulation

As an example of sequentially determining M⁽²⁾ and x₁, the action 204may be performed by arbitrarily or randomly assuming a modulation{circumflex over (M)}⁽²⁾ of the interfering signal. In this case,{circumflex over (M)}⁽²⁾=K, where K is an arbitrarily assumed modulationbelonging to the known set ψ of available modulations. The desiredsignal x₁ may be calculated in the action 206 by performing ML detectionbased on this assumption of a fixed modulation for the interferingsignal, as follows:

$\begin{matrix}{{\hat{x}}_{1} = {\underset{{x_{1} \in \Omega^{(M^{(1)})}},{x_{2} \in \Omega^{(M^{(2)})}}}{\arg\;\min}{{r - {h_{1}x_{1}} - {h_{2}x_{2}}}}^{2}}} & {{Equation}\mspace{14mu} 20}\end{matrix}$

As another example of sequentially determining M⁽²⁾ and x₁, the action204 may be performed by assuming the superposition of all possiblemodulations of the interfering signal. Thus, assuming that the known setψ of modulations comprises, for example, QPSK, QAM16, and QAM64modulations, the action 206 of calculating the desired signal x₁ can becalculated by performing ML detection as follows:

$\begin{matrix}{{\hat{x}}_{1} = {\underset{{x_{1} \in \Omega^{(M^{(1)})}};{x_{2} \in {\Omega^{({QPSK})}\bigcup\Omega^{({{QAM}\; 16})}\bigcup\Omega^{({{QAM}\; 64})}}}}{\arg\;\min}{{r - {h_{1}x_{1}} - {h_{2}x_{2}}}}^{2}}} & {{Equation}\mspace{14mu} 21}\end{matrix}$Linear Receiver with Modulation Detection

Sequential determination of M⁽²⁾ and x₁ may also be performed byimplementing a linear receiver for the interfering signal x₂, to detectthe modulation {circumflex over (M)}⁽²⁾ of x₂. The action 204 may beperformed by a linear receiver using MMSE, zero-forcing (ZF), maximalratio combining (MRC), and/or other types of linear detection schemes.In an embodiment, an MMSE receiver may implemented as follows:

$\begin{matrix}{{{\overset{\sim}{x}}_{2} = {{C_{tmear}r}\overset{MMSE}{=}{{\alpha\;{h_{2}^{H}( {{HH}^{H} + {\sigma^{2}I}} )}^{- 1}r} = {x_{2} + z}}}}{\sigma_{z}^{2} = {{E\lbrack {z}^{2} \rbrack} = \frac{1}{{SINR}_{MMSE}}}}} & {{Equation}\mspace{14mu} 22}\end{matrix}$

Based on Equation 22, a modulation detector may be implemented todetermine an assumed modulation {circumflex over (M)}⁽²⁾ of theinterfering signal x₂ as follows:

$\begin{matrix}{{\hat{M}}^{(2)} = {\underset{M^{(2)} \in \Psi}{\arg\;\min}\;{J( {{{{\hat{x}}_{2}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\overset{\sim}{x}}_{2}(L)}},M^{(2)},\sigma_{z}^{2}} )}}} & {{Equation}\mspace{14mu} 23}\end{matrix}$The optimization function J(.) can be derived from the counterpartequations [9] to [18]. The analogy between the optimization functions isthat h₁ and x₁ equal to 0, and h₂ equal to 1, also r equal to {tildeover (x)}₂. In other word, the relationship between the transmitted andreceived signals in Equation [1] and [2] is reduced to a single usercase, where the received signals are the output of the linear receiver{tilde over (x)}₂ and the transmitted signal x₂ travels through an AWGNchannel in absence of x₁. By applying the principle of modulationdetection, or a-posteriori probability determination in Equation [9] to[18] for the new input-output relationship, the modulation of x₂ may bedetermined accordingly. For example, the log-MAP style function is asfollows:

${\hat{M}}_{2} = {\underset{M_{2} \in \psi}{\arg\;\max}( \frac{1}{M^{(2)}} )^{L}{\prod\limits_{i = 1}^{L}\;( {\sum\limits_{n = 1}^{M^{(2)}}{\exp( {- \frac{{{{{\overset{\sim}{x}}_{2}(i)} - x_{2}^{(n)}}}^{2}}{\sigma_{z}^{2}}} )}} )}}$The technique of sum-max-log-MAP, mean-max-log-MAP, max-log-MAP-scale,log-MAP-best-K can be applied similarly.

After determining the assumed modulation of the interfering signal inthis manner, the desired signal x₁ may be calculated in the action 206by using ML detection as follows:

$\begin{matrix}{{\hat{x}}_{1} = {\underset{{x_{1} \in \Omega^{(M^{(1)})}},{x_{2} \in \Omega^{({\hat{M}}^{(2)})}}}{\arg\;\min}{{r - {h_{1}x_{1}} - {h_{2}x_{2}}}}^{2}}} & {{Equation}\mspace{14mu} 24}\end{matrix}$LLR-Based Modulation Detection

The modulation of the interfering signal may also be determined orestimated in the action 204 based on a soft decision or log-likelihoodratio (LLR) of each bit at the output of a receiver, as follows:

$\begin{matrix}{{LLR}_{k} = {\log( \frac{P( {{b_{1}(k)} =  1 \middle| r } )}{P( {{b_{1}(k)} =  0 \middle| r } )} )}} & {{Equation}\mspace{14mu} 25}\end{matrix}$

Instead of examining the probability of each symbol in a transmittedsequence, this technique evaluates the probability of each coded bit:

$\begin{matrix}{{{LLR}_{k}^{(M^{(2)})} = {\log( \frac{P( {{{b_{1}(k)} =  1 \middle| r },M^{(2)}} )}{P( {{{b_{1}(k)} =  0 \middle| r },M^{(2)}} )} )}}{{\hat{M}}^{(2)} = {\underset{M_{2} \in \Psi}{\arg\;\max}{J( {{LLR}_{k},M^{(2)}} )}}}} & {{Equation}\mspace{14mu} 26}\end{matrix}$

For example, the following metric can represent the probability of acorrectly decoded bit sequence:

$\begin{matrix}{{J( M^{(2)} )} = {\prod\limits_{k = 1}^{L^{\prime}}\;\frac{1}{1 + {\exp( {- {{LLR}_{k}^{(M^{(2)})}}} )}}}} & {{Equation}\mspace{14mu} 27}\end{matrix}$

Alternatively, an approximate and simpler solution is as follows:

$\begin{matrix}{{J( M^{(2)} )} = {\prod\limits_{k = 1}^{L^{\prime}}{{LLR}_{k}^{(M^{(2)})}}}} & {{Equation}\mspace{14mu} 28}\end{matrix}$Joint Decoding and Modulation Detection

In cases where signals x comprise coded symbols, modulation detectionand decoding can be performed jointly or concurrently to determine themost probable modulation {circumflex over (M)}⁽²⁾ of the interferingsignal x₂. For example, multiple ML detection may be performed inparallel or in sequence, based on all possible M⁽²⁾ modulations. Theresulting outputs may then be fed to channel decoders to produce decodedsequences. Assuming that the symbols of the sequences have errorencoding, such as cyclical redundancy check (CRC) encoding, the decodedsequences may be examined to determine whether they exhibit errors. Theoutput sequence exhibiting the lowest error rate or the highest accuracymay then be selected, and the modulation M resulting in that outputsequence may be assumed to be the modulation M⁽²⁾ of the interferingsignal.

When CRC is not available, other approaches may be used to select theoutput sequence that is most likely to be correctly decoded. Forexample, the mean of the absolute information bit LLR may be used todetermine whether an output stream has been correctly decoded.

After determining M⁽²⁾ in this manner, ML detection may be applied inthe action 206 to determine the desired signal x₁ based on thedetermined modulation M⁽²⁾ of the interfering signal.

Hard Decision Distribution Metric

In an embodiment, the constellation distribution of hard decisions in anMMSE receiver, applied to the interfering channel x₂, may be evaluatedto determine the likely validity of the previously described decisionsregarding M⁽²⁾. For example, the hard decisions regarding theinterfering channel x₂ may be evaluated to determine whether theyexhibit expected constellation distributions for the assumed modulation{circumflex over (M)}⁽²⁾.

For example, a hard slicer may be applied at the output of a linearreceiver such as an MMSE receiver, as follows:{circumflex over (x)} ₂ =Q(αh ₂ ^(H)(HH ^(H)+σ² I)⁻¹ r;M ⁽²⁾)

Equation 29

A typical transmitted signal uniformly occupies the constellation. Ifthe hard decision at the output of hard slicer shows a non-uniformdistribution in the assumed constellation, the likelihood of thisassumed modulation, P(M⁽²⁾), may decrease, or this assumption may becomeinvalid.

For example, for M⁽²⁾>4, the numbers of hard decisions in differentsubsets of the constellation of the assumed modulation {circumflex over(M)}⁽²⁾ are calculated, and these numbers are compared to expecteddistributions within the assumed modulation {circumflex over (M)}⁽²⁾. Asan example, the constellation of the assumed modulation {circumflex over(M)}⁽²⁾ may be partitioned into two subsets, and the following ratio maybe calculated:

$\begin{matrix}{\rho^{(M_{2})} = \frac{\{ {{{\hat{x}}_{2}(i)}:{{\hat{x}}_{2}( {i \in \Omega_{1} \Subset \Omega^{(M^{(2)})}} )}} \} }{\{ {{{\hat{x}}_{2}(i)}:{{\hat{x}}_{2}( {i \in \Omega_{2} \Subset \Omega^{(M^{(2)})}} )}} \} }} & {{Equation}\mspace{14mu} 30}\end{matrix}$

In Equation 30, subset 1 (Ω₁) includes constellation points of theassumed modulation {circumflex over (M)}⁽²⁾ that are relatively likelyif the assumed modulation is actually used by the interfering signal x₂.Subset 2 (Ω₂) includes the points that relatively likely if theestimated modulation {circumflex over (M)}⁽²⁾ is not used by theinterfering signal x₂.

The ratio of Equation 30 can be compared to a threshold to determinewhether the observed distribution is incorrectly biased relative to theexpected distribution of the assumed modulation {circumflex over(M)}⁽²⁾, as follows:ρ^((M) ² ⁾>ρ_(threshold)

Equation 30A

The decision based on Equation 30A may be used to qualify or refinedecisions regarding {circumflex over (M)}⁽²⁾ made using the Joint MLSequence and Modulation Detection or sequential modulation and sequencedetection schemes discussed above.

Subsampled Sequences

In the various techniques and methods described above, the interferingchannel x₂ may be subsampled in order to reduce the number ofcalculations involved in any given technique. For example, r may besampled once in every 100 symbols over a received sequence of 10000symbols, in situations in which the modulation is anticipated to remainconstant over at least 10000 symbols.

More particularly, the subsampling may be performed as follows:

$\begin{matrix}{{\hat{M}}^{(2)} = {\underset{M^{(2)},x_{1},x_{2}}{\arg\;\max}( \frac{1}{M^{(2)}} )^{L}{\quad{\prod\limits_{j = 1}^{L^{\prime}}( {\sum\limits_{m = 1}^{M^{(1)}}{\sum\limits_{n = 1}^{M^{(2)}}{\exp( {- \frac{{{{r(i)} - {{h_{1}( i_{j} )}x_{1}^{(m)}} - {{h_{2}( i_{j} )}x_{2}^{(n)}}}}^{2}}{\sigma^{2}}} )}}} )}}}} & {{Equation}\mspace{14mu} 31}\end{matrix}$In addition to applying to log-MAP in Equation 31, subsampling can beapplied to the technique of sum-max-log-MAP, mean-max-log-MAP,max-log-MAP-scale, log-MAP-bests-K similarly. Subsampling can be also beapplied in combination with other techniques such as hard decisiondistribution metric discussed above, and adaptive ML/MMSE hereafter.Adaptive ML/MMSE

In certain embodiments, the ML detection schemes described above may beused when they produce reliable or accurate results, and MMSE may beused in other situations, where noise dominates or the modulationdetection is deemed to be unreliable. Various measures or metrics may beused to determine whether the ML modulation detection is or is likely tobe reliable and/or accurate. For example, signal-to-noise ratio (SNR)and/or SINR (signal-to-interference/noise ratio) of the interferingchannel x₂ may be used as indicators of detection reliability.

As another example, the hard decision matrix metric described above, orany other observed quality metric, may be used in determining the actualaccuracy or reliability of modulation detection. More specifically, theratio of the minimum and maximum metrics may be evaluated and comparedto a threshold to indicate the likely reliability of modulationdetection, as follows:

$\begin{matrix}{\theta = {\frac{\max\limits_{M^{(2)} \in \Psi}{J( M^{(2)} )}}{\min\limits_{M^{(2)} \in \Psi}{J( M^{(2)} )}} \leq \theta_{0}}} & {{Equation}\mspace{14mu} 32}\end{matrix}$Interference Cancellation without Modulation Knowledge

Typically, interference cancellation requires knowledge of themodulation of the interfering signal as well as coding information forthe interfering signal. However, the following technique can be used insituations in which the modulation information of the interfering signalis not known at the receiver.

FIG. 3 shows an example signal flow, illustrating interferencecancellation without knowledge of the modulation of the interferingsignal. The signal r is received at block 302, which selects one of theavailable or possible modulations M⁽²⁾ for x₂. A MIMO receiver 304 isapplied to the interfering signal x₂ and used as the basis forcalculating the a-posteriori probability of the selected M⁽²⁾. Theestimated probabilities for all available modulations are provided to ablock 306 that calculations soft estimates of the interfering signal x₂.These are subtracted from the incoming signal r, and the resultingsignal is provided to a MIMO receiver 308 for x₁, and a subsequentchannel decoder 310 for x₁. If coding information is available for theinterfering signal x₂, a channel decoder 312 may be used and its outputprovided to the soft estimation 306.

As an example of this process, assuming that coding information for theinterfering signal x₂ is not available, a linear MMSE receiver may beapplied to equalize the MIMO channel as follows:{tilde over (x)} ₂ =αh ₂ ^(H)(HH ^(H)+σ² I)⁻¹ r=x ₂ +z

Equation 33

Based on Equation 31, the probability of each available modulation maybe calculated as follows:

$\begin{matrix}{{P( M^{(2)} \middle| r )} = {{{c \cdot {P( r \middle| M^{(2)} )}} \approx {c_{1} \cdot {\exp( {{{- \frac{1}{L}}{\sum\limits_{i = 1}^{L}\frac{{{{{\overset{\sim}{x}}_{2}(i)} - {{\hat{x}}_{2}(i)}}}^{2}}{\sigma_{z}^{2}(i)}}} - {\log( M^{(2)} )}} )}}} = \frac{\exp( {{{- \frac{1}{L}}{\sum\limits_{i = 1}^{L}\frac{{{{{\overset{\sim}{x}}_{2}(i)} - {{\hat{x}}_{2}(i)}}}^{2}}{\sigma_{z}^{2}(i)}}} - {\log( M^{(2)} )}} )}{\sum\limits_{M_{2} \in \Psi}{\exp( {{{- \frac{1}{L}}{\sum\limits_{i = 1}^{L}\frac{{{{{\overset{\sim}{x}}_{2}(i)} - {{\hat{x}}_{2}(i)}}}^{2}}{\sigma_{z}^{2}(i)}}} - {\log( M^{(2)} )}} )}}}} & {{Equation}\mspace{14mu} 34}\end{matrix}$

It is to be noted that the Sum-Max-Log-MAP, as described above, is usedin Equation 34 to approximate probabilities. Other described methods,such as Log-MAP, Max-Log-MAP-Scale, and so forth can also be appliedhere.

It is to also be noted that FIG. 3 is an example in which theprobability of modulation is calculated after a linear receiver isapplied to detect the interfering signal in Equations 33 and 34. Thea-posteriori probability of the modulation can also be calculateddirectly from the received signals, as in the previous examples ofEquation 19. Other described methods, such as Log-MAP,Max-Log-MAP-Scale, Sum/Mean-Max-Log-MAP can also be applied here.

The soft estimation for each hypothesized modulation may then becalculated as follows:

$\begin{matrix}{{E\lbrack { {x_{2}(i)} \middle| r ,M^{(2)}} \rbrack} = {{\sum\limits_{x_{2} \in \Omega^{(M^{(2)})}}{x_{2} \cdot {P( { x_{2} \middle| {r(i)} ,M^{(2)}} )}}} = \frac{\sum\limits_{x_{2} \in \Omega^{(M^{(2)})}}( {x_{2}{\exp( {- \frac{{{{{\overset{\sim}{x}}_{2}(i)} - x_{2}}}^{2}}{\sigma_{z}^{2}(i)}} )}} )}{\sum\limits_{x_{2} \in \Omega^{(M^{(2)})}}{\exp( {- \frac{{{{{\overset{\sim}{x}}_{2}(i)} - x_{2}}}^{2}}{\sigma_{z}^{2}(i)}} )}}}} & {{Equation}\mspace{14mu} 35}\end{matrix}$

Again, the different kinds of Log-MAP techniques described above can beapplied to Equation 35 to lower computational complexities ofdetermining the probability of every possible modulated symbol in eachmodulation.

The soft estimation is then calculated over all possible modulations, asfollows:

$\begin{matrix}{{{\hat{x}}_{2}(i)} = {{E\lbrack {x_{2}(i)} \middle| r \rbrack} = {\sum\limits_{M^{(2)} \in \Psi}{{E\lbrack { x_{2} \middle| r ,M^{(2)}} \rbrack}{P( M^{(2)} \middle| r )}}}}} & {{Equation}\mspace{14mu} 36}\end{matrix}$

Based on the soft estimation of Equation 36, the interference may becanceled from the interfering signal x₂, and the desired signal x₁ maybe decoded as follows:{tilde over (r)}(i)=r(i)−h ₂ {circumflex over (x)} ₂(i)

{circumflex over (x)} ₁(i)

Equation 37

Both FIG. 2 and FIG. 3 involve determining a-posteriori probability ofthe modulation for the interfering signal. An example difference of thetechniques of FIGS. 2 and 3 can be interpreted as a hard and softdetection of the modulation. The techniques in FIG. 2 and the associatedvariations, for example, make a hard decision on the modulation for theinterfering signals based on a-posteriori probabilities and then rely ona modulation-dependent MIMO detection (e.g., ML detection) given themodulation of the interfering signals for interference suppression, orperformance improvement. The techniques in FIG. 3 and the associatedvariations, for example, do not need to determine the modulation of theinterfering signals. Instead, the expectation of the interfering signal,or a “soft” usage of the modulation of the interfering signals, is usedfor interference cancellation in FIG. 3.

A combination of hard and soft modulation detection is possible, wherethe modulation of the interfering signal is determined, and then a softexpectation of the interfering signals is cancelled from the receivedsignal. This can be interpreted as another example of modulationdependent MIMO detection.

Another combination of soft and hard modulation detection is to find ahard estimate of modulated symbols in each modulation, and determine theexpected interfering signals as the sum of each modulated symbolsweighted by the probability of the modulation. This can be interpretedas the Sum-Max-Log-MAP technique for determining the probability of eachmodulated symbol given the modulation.

CONCLUSION

The discussion above assumes two rank-1 users for ease of description.However, the described techniques are not limited to the number of usersor the ranks of the users. Rather, the described techniques can beextended to arbitrary numbers of users with arbitrary ranks, where theeffective channels are available but the modulations of the channels areunknown at the receiver. For example, ML sequence and modulationdetection with Sum-Max-Log-Map techniques may be performed with respectto multiple users in accordance with the following:

$\begin{matrix}{\{ {{{{\hat{x}}_{1}(1)}\mspace{14mu}\ldots\mspace{14mu}{{\hat{x}}_{1}(L)}},{{\hat{M}}^{(2)}\ldots\mspace{14mu}{\hat{M}}^{(K)}}} \} = {\quad{\underset{{M^{(1)}\ldots\mspace{14mu} M^{(K)}},{x_{1}\ldots\mspace{14mu} x_{K}}}{\arg\;\min}( {{\frac{1}{L}{\sum\limits_{i = 1}^{L}\frac{\begin{matrix}{\min{{{r(i)} - {{H_{1}(i)}{x_{1}^{(m_{1})}(i)}} -}}} \\{{{{H_{2}(i)}{x_{2}^{(m_{2})}(i)}\mspace{20mu}\ldots}\mspace{14mu} - {{H_{K}(i)}{x_{K}^{(m_{x})}(i)}}}}^{2}\end{matrix}}{\sigma^{2}}}} + {\sum\limits_{k = 2}^{K}{\log( M^{(k)} )}}} )}}} & {{Equation}\mspace{14mu} 38}\end{matrix}$

In the case of more than 1 interfering signals, the detection of themodulation for each signal can be done jointly by solving equation 38,for example if Sum-Max-Log-MAP technique is applied. Alternatively (oradditionally), the modulation of each signal can be successivelydetected, treating all the signals with modulations undetected as noise.Another alternative is to detect the modulation of each signal inparallel, treating all the rest signals with modulations as noise.Furthermore, the joint and successive/parallel modulation detection formore than 1 interfering signals can be applied at the same time, forexample, by grouping interfering signals, and applying the joint orsuccessive/parallel principle to each group, and then applying the jointor successive/parallel principle within each group.

The discussion above also assume the existence of the interferingsignals has been identified. Alternatively, the existence of theinterfering signals can be included in the modulation detection byassigning the non-existence of the interfering signals as a specialmodulation and included in the set of modulations ψ. If the modulationof “non-existence” has been identified as the most likely one, thereceiver may deem that the interfering signals are not present.

Note that the description above incorporates use of the phrases “in anembodiment,” or “in various embodiments,” or the like, which may eachrefer to one or more of the same or different embodiments. Furthermore,the terms “comprising,” “including,” “having,” and the like, as usedwith respect to embodiments of the present disclosure, are synonymous.

As used herein, the terms “logic,” “component,” and “module” may referto, be part of, or include an Application Specific Integrated Circuit(ASIC), an electronic circuit, a processor (shared, dedicated, or group)and/or memory (shared, dedicated, or group) that execute one or moresoftware or firmware programs, a combinational logic circuit, and/orother suitable components that provide the described functionality. Thelogic and functionality described herein may be implemented by any suchcomponents.

In accordance with various embodiments, an article of manufacture may beprovided that includes a storage medium having instructions storedthereon that, if executed, result in the operations described above. Inan embodiment, the storage medium comprises some type of non-transitorymemory (not shown). In accordance with various embodiments, the articleof manufacture may be a computer-readable medium such as, for example,software or firmware.

Various operations may have been described as multiple discrete actionsor operations in turn, in a manner that is most helpful in understandingthe claimed subject matter. However, the order of description should notbe construed as to imply that these operations are necessarily orderdependent. In particular, these operations may not be performed in theorder of presentation. Operations described may be performed in adifferent order than the described embodiment. Various additionaloperations may be performed and/or described operations may be omittedin additional embodiments.

Although certain embodiments have been illustrated and described herein,a wide variety of alternate and/or equivalent embodiments orimplementations calculated to achieve the same purposes may besubstituted for the embodiments illustrated and described withoutdeparting from the scope of the present disclosure. This application isintended to cover any adaptations or variations of the embodimentsdiscussed herein. Therefore, it is manifestly intended that embodimentsin accordance with the present disclosure be limited only by the claimsand the equivalents thereof.

What is claimed is:
 1. A method, comprising: receiving, at a receiver, adesired signal and an interfering signal, wherein the interfering signalwas transmitted with a modulation unknown to the receiver; identifying alikely modulation corresponding to the modulation with which theinterfering signal was transmitted; and decoding the desired signalusing a modulation dependent multiple-input multiple output (MIMO)detection, wherein the modulation dependent MIMO detection is based atleast in part on the identified likely modulation corresponding to themodulation with which the interfering signal was transmitted, whereinthe modulation dependent MIMO detection includes maximum likelihood (ML)detection.
 2. The method of claim 1, wherein identifying the likelymodulation comprises arbitrarily selecting a modulation from a set ofpossible modulations.
 3. The method of claim 1, wherein identifying thelikely modulation comprises calculating a superposition of multiplepossible modulations of the interfering signal.
 4. The method of claim1, wherein identifying the likely modulation comprises applying a linearreceiver to the interfering signal to identify the likely modulation. 5.The method of claim 4, further comprising: detecting the interferingsignal using the linear receiver multiple times, such that for each timeof the multiple times, a corresponding possible modulation from a set ofmodulations is assumed while detecting the interfering signal using thelinear receiver for the corresponding time; and maximizing ana-posteriori probability of each of the possible modulations from theset of modulations.
 6. The method of claim 5, wherein maximizing thea-posteriori probability of the possible modulation from the set ofmodulations and detecting the interfering signal using the linearreceiver is based at least in part on assuming that the possiblemodulation is simplified by minimizing a minimum distance offset by avalue that is based on one or both of the possible modulation and aresidual noise.
 7. The method of claim 5, wherein maximizing thea-posteriori probability of the possible modulation from the set ofmodulations and detecting the interfering signal using the linearreceiver is based at least in part on assuming that the possiblemodulation is simplified by maximizing at least in part a sum of largestK conditional probabilities.
 8. The method of claim 5, wherein ana-priori probability of the possible modulation is scaled at least inpart in accordance with a distribution of the interfering signaldetected by the linear receiver assuming the possible modulation.
 9. Themethod of claim 1, wherein: identifying the likely modulation comprisesdecoding the interfering signal multiple times using differentmodulations from a set of possible modulations; and determining which ofthe modulations results in accurate decoding of the interfering signal.10. The method of claim 1, wherein identifying the likely modulation anddecoding the desired signal comprise maximizing an a-posterioriprobability of the desired signal and a possible modulation from a setof modulations.
 11. The method of claim 10, wherein maximizing thea-posteriori probability is simplified at least in part by minimizing aminimum distance offset by a value, and wherein the value is based onone or more of the possible modulation, a noise power, and a conditionof a channel.
 12. The method of claim 10, wherein maximizing thea-posteriori probability is simplified by maximizing at least in part asum of the largest K conditional probabilities.
 13. The method of claim1, wherein identifying the likely modulation further comprises selectingat least in part a subset of the received signal.
 14. The method ofclaim 1, wherein identifying the likely modulation further comprises:determining a reliability of the likely modulation identified; andreplacing the modulation dependent MIMO detection by a linear receiver.15. The method of claim 1, wherein the interfering signal is a firstinterfering signal, and wherein the method further comprises: receiving,at the receiver, a plurality of interfering signals including the firstinterfering signal, wherein each of the plurality of interfering signalswere transmitted with a respective modulation unknown to the receiver;and identifying a plurality of likely modulations corresponding to aplurality of modulations with which the plurality of interfering signalswere respectively transmitted, wherein the modulation dependent MIMOdetection is based at least in part on the identified plurality oflikely modulations corresponding to the plurality of modulations withwhich the plurality of interfering signals were respectivelytransmitted.
 16. A system comprising: one or more antennas configured toreceive a desired signal and an interfering signal, wherein theinterfering signal was transmitted with a modulation unknown to thesystem; and a data processing unit coupled to the one or more antennas,wherein the data processing unit is configured to (i) identify a likelymodulation corresponding to the modulation with which the interferingsignal was transmitted, and (ii) decode the desired signal using amodulation dependent detection, wherein the modulation dependentdetection is based at least in part on the identified likely modulationcorresponding to the modulation with which the interfering signal wastransmitted, wherein the modulation dependent detection includes maximumlikelihood (ML) detection.
 17. The system of claim 16, wherein the dataprocessing unit is further configured to identify the likely modulationby arbitrarily selecting a modulation from a set of possiblemodulations.
 18. The system of claim 16, wherein the data processingunit is further configured to identify the likely modulation bycalculating a superposition of multiple possible modulations of theinterfering signal.
 19. The system of claim 16, wherein the dataprocessing unit is further configured to identify the likely modulationby applying a linear receiver to the interfering signal.
 20. The systemof claim 16, wherein the data processing unit is further configured toidentify the likely modulation by (i) detecting the interfering signalusing the linear receiver multiple times, such that for each time of themultiple times, a corresponding possible modulation from a set ofmodulations is assumed while detecting the interfering signal using thelinear receiver for the corresponding time, and (ii) maximizing ana-posteriori probability of each of the possible modulations from theset of modulations.
 21. The system of claim 20, wherein the dataprocessing unit is further configured to maximize the a-posterioriprobability of the possible modulation and detect the interferingsignals by: minimizing at least in part a minimum distance offset by avalue, wherein the value is based on one or more of the possiblemodulation and a residual noise.
 22. The system of claim 20, wherein thedata processing unit is further configured to maximize the a-posterioriprobability of the possible modulation and detect the interferingsignals by: maximizing at least in part a sum of the largest Kconditional probabilities.
 23. The system of claim 20, wherein the dataprocessing unit is further configured to scale an a-priori probabilityof the possible modulation based at least in part on a distribution ofthe interfering signal detected by the linear receiver assuming thepossible modulation.
 24. The system of claim 16, wherein the dataprocessing unit is further configured to identify the likely modulationby (i) decoding the interfering signal multiple times using differentmodulations from a set of possible modulations and (ii) determiningwhich of the modulations results in accurate decoding of the interferingsignal.
 25. The system of claim 16, wherein the data processing unit isfurther configured to identify the likely modulation and decode thedesired signal by maximizing an a-posteriori probability of the desiredsignal and a possible modulation from a set of modulations.
 26. Thesystem of claim 25, wherein the data processing unit is furtherconfigured to simplify maximizing the a-posteriori probability at leastin part by minimizing the minimum distance offset by a value, andwherein the value depends on one or more factors that are the possiblemodulation, the noise power, the channels.
 27. The system of claim 25,wherein the data processing unit is further configured to simplifymaximizing the a-posteriori probability by maximizing at least in part asum of the largest K conditional probabilities.
 28. The system of claim16, wherein the data processing unit is further configured to select atleast in part a subset of received signals to identify the likelymodulation.
 29. The system of claim 16, wherein the data processing unitis further configured to (i) determine a reliability of the likelymodulation, and (ii) based on determine the reliability of the likelymodulation, replace the modulation dependent MIMO detection with adetection using a linear receiver.